activity
20182022
most citedAbstract Reasoning with Distracting Features

30 citations · 54 across the 5 of their papers we have counts for

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Showing cs.LGShow all

10 papers · 1 filter

cs.LG20221 cited

Meta-CPR: Generalize to Unseen Large Number of Agents with Communication Pattern Recognition Module

Wei-Cheng Tseng, Wei Wei, Da-Cheng Juan +1

Designing an effective communication mechanism among agents in reinforcement learning has been a challenging task, especially for real-world applications. The number of agents can…

cs.LG2019

COCO-GAN: Generation by Parts via Conditional Coordinating

Chieh Hubert Lin, Chia-Che Chang, Yu-Sheng Chen +3

Humans can only interact with part of the surrounding environment due to biological restrictions. Therefore, we learn to reason the spatial relationships across a series of observa…

cs.LG201915 cited

Complement Objective Training

Hao-Yun Chen, Pei-Hsin Wang, Chun-Hao Liu +5

Learning with a primary objective, such as softmax cross entropy for classification and sequence generation, has been the norm for training deep neural networks for years. Although…

cs.LG2019

Improving Adversarial Robustness via Guided Complement Entropy

Hao-Yun Chen, Jhao-Hong Liang, Shih-Chieh Chang +4

Adversarial robustness has emerged as an important topic in deep learning as carefully crafted attack samples can significantly disturb the performance of a model. Many recent meth…

cs.LG2018

InstaNAS: Instance-aware Neural Architecture Search

An-Chieh Cheng, Chieh Hubert Lin, Da-Cheng Juan +2

Conventional Neural Architecture Search (NAS) aims at finding a single architecture that achieves the best performance, which usually optimizes task related learning objectives suc…

cs.LG2018

Policy Certificates: Towards Accountable Reinforcement Learning

Christoph Dann, Lihong Li, Wei Wei +1

The performance of a reinforcement learning algorithm can vary drastically during learning because of exploration. Existing algorithms provide little information about the quality…